The Lauritzen-Chen Likelihood For Graphical Models

TL;DR

This paper introduces a new likelihood framework for graphical models that unifies Markov random fields and Bayesian networks, ensuring consistency within Markov equivalence classes and aiding model selection.

Contribution

It links Chen's density decomposition with Lauritzen's clique factorization to create a general, non-redundant likelihood applicable to parametric and semi-parametric models, including DAGs.

Findings

Provides a likelihood that is variationally independent and non-redundant.

Ensures likelihood consistency across Markov equivalent DAGs.

Facilitates model selection and semi-parametric inference.

Abstract

Graphical models such as Markov random fields (MRFs) that are associated with undirected graphs, and Bayesian networks (BNs) that are associated with directed acyclic graphs, have proven to be a very popular approach for reasoning under uncertainty, prediction problems and causal inference. Parametric MRF likelihoods are well-studied for Gaussian and categorical data. However, in more complicated parametric and semi-parametric settings, likelihoods specified via clique potential functions are generally not known to be congenial {(jointly well-specified)} or non-redundant. Congenial and non-redundant DAG likelihoods are far simpler to specify in both parametric and semi-parametric settings by modeling Markov factors in the DAG factorization. However, DAG likelihoods specified in this way are not guaranteed to coincide in distinct DAGs within the same Markov equivalence class. This complicates likelihoods based model selection procedures for DAGs by ``sneaking in'' potentially unwarranted assumptions about edge orientations. In this paper we link a density function decomposition due to Chen with the clique factorization of MRFs described by Lauritzen to provide a general likelihood for MRF models. The proposed likelihood is composed of variationally independent, and non-redundant closed form functionals of the observed data distribution, and is sufficiently general to apply to arbitrary parametric and semi-parametric models. We use an extension of our developments to give a general likelihood for DAG models that is guaranteed to coincide for all members of a Markov equivalence class. Our results have direct applications for model selection and semi-parametric inference.